This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

About this Dataset

The data contains the following information:

Country- this is the country for which the vaccination information is provided; Country ISO Code - ISO code for the country; Date - date for the data entry; for some of the dates we have only the daily vaccinations, for others, only the (cumulative) total; Total number of vaccinations - this is the absolute number of total immunizations in the country; Total number of people vaccinated - a person, depending on the immunization scheme, will receive one or more (typically 2) vaccines; at a certain moment, the number of vaccination might be larger than the number of people; Total number of people fully vaccinated - this is the number of people that received the entire set of immunization according to the immunization scheme (typically 2); at a certain moment in time, there might be a certain number of people that received one vaccine and another number (smaller) of people that received all vaccines in the scheme; Daily vaccinations (raw) - for a certain data entry, the number of vaccination for that date/country; Daily vaccinations - for a certain data entry, the number of vaccination for that date/country; Total vaccinations per hundred - ratio (in percent) between vaccination number and total population up to the date in the country; Total number of people vaccinated per hundred - ratio (in percent) between population immunized and total population up to the date in the country; Total number of people fully vaccinated per hundred - ratio (in percent) between population fully immunized and total population up to the date in the country; Number of vaccinations per day - number of daily vaccination for that day and country; Daily vaccinations per million - ratio (in ppm) between vaccination number and total population for the current date in the country; Vaccines used in the country - total number of vaccines used in the country (up to date); Source name - source of the information (national authority, international organization, local organization etc.); Source website - website of the source of information;

Tasks: Track the progress of COVID-19 vaccination What vaccines are used and in which countries? What country is vaccinated more people? What country is vaccinated a larger percent from its population?

This data is valuble in relation to the health, financial, and engineering sectors.

Category

Health & Medicine

Keywords

Health,Medicine,covid-19,dataset,progress

Row Count

5824

Price

$120.00

Analysis of ‘COVID vaccination vs. mortality ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sinakaraji/covid-vaccination-vs-death on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

The COVID-19 outbreak has brought the whole planet to its knees.More over 4.5 million people have died since the writing of this notebook, and the only acceptable way out of the disaster is to vaccinate all parts of society. Despite the fact that the benefits of vaccination have been proved to the world many times, anti-vaccine groups are springing up all over the world. This data set was generated to investigate the impact of coronavirus vaccinations on coronavirus mortality.

Content

Data Collection

This dataset is a combination of the following three datasets:

1.https://www.kaggle.com/gpreda/covid-world-vaccination-progress

2.https://covid19.who.int/WHO-COVID-19-global-data.csv

3.https://www.kaggle.com/rsrishav/world-population

you can find more detail about this dataset by reading this notebook:

https://www.kaggle.com/sinakaraji/simple-linear-regression-covid-vaccination

Countries in this dataset:

--- Original source retains full ownership of the source dataset ---

IntroductionIn relatively wealthy countries, substantial between-country variability in COVID-19 vaccination coverage occurred. We aimed to identify influential national-level determinants of COVID-19 vaccine uptake at different COVID-19 pandemic stages in such countries.MethodsWe considered over 50 macro-level demographic, healthcare resource, disease burden, political, socio-economic, labor, cultural, life-style indicators as explanatory factors and coverage with at least one dose by June 2021, completed initial vaccination protocols by December 2021, and booster doses by June 2022 as outcomes. Overall, we included 61 European or Organisation for Economic Co-operation and Development (OECD) countries. We performed 100 multiple imputations correcting for missing data and partial least squares regression for each imputed dataset. Regression estimates for the original covariates were pooled over the 100 results obtained for each outcome. Specific analyses focusing only on European Union (EU) or OECD countries were also conducted.ResultsHigher stringency of countermeasures, and proportionately more older adults, female and urban area residents, were each strongly and consistently associated with higher vaccination rates. Surprisingly, socio-economic indicators such as gross domestic product (GDP), democracy, and education had limited explanatory power. Overall and in the OECD, greater perceived corruption related strongly to lower vaccine uptake. In the OECD, social media played a noticeable positive role. In the EU, right-wing government ideology exhibited a consistently negative association, while cultural differences had strong overall influence.ConclusionRelationships between country-level factors and COVID-19 vaccination uptake depended on immunization stage and country reference group. Important determinants include stringency, population age, gender and urbanization, corruption, government ideology and cultural context.

Context

To be honest it's pretty hard for you to find data on vaccine progress and especially time-based data on a country like Pakistan. So, I created this small but interactive notebook that will keep updating the database until everyone is vaccinated. In this project I have used Pandas for easy WebSracping to get the data from pharmaceutical-technology.com then I have created Sqlite3 database to store the data into three tables. It took me a few tries to get everything working smooth so I started using SQL queries to get the data and then used plotly to plot interactive visualization. I was not sure when they will update the website so, I have created few functions to avoid duplication of data and to inform me on telegram about updates. I have also uploaded the processed data to Kaggle from Deepnote which will be updated daily. At last, I have used the Deepnote Schedule notebook feature to run this notebook every day and successfully publishing the article You can find my work on Deepnote.

Content

• World_Vaccination_Progress.csv -> Countries Vaccination progress
• pakistan_time_series.csv -> Time series data of Pakistan vaccine progress
• world_time_series.csv -> Time series data of World vaccine progress

Columns: - Country :: Names of countries in the world - Doses Administered: Total Doses Administered - Doses per 1000 : Number of Doses per thousand - Fully Vaccinated Population (%) : Percentage of a fully vaccinated person in a country. - Vaccine being used in a country : Types of vaccines used in a country.

For Time-Series

• Date_Time : Timestamp of entry

Acknowledgements

I am thankful for Pharmaceutical Technology for updating the stats on daily basis and publicly provide real-time stats of world's vaccination drive. I also want to thank Deepnote for the introduction of the Schedule notebook feature that has made this automation possible.

Inspiration

The lack of data available in my country drove me to create an automated system that collects data from web. You can read more about it in my article. The second inspiration came from participating in Deepnote competition which was on the data Vaccination drive of your country or World.

ObjectivesWe assessed the knowledge and awareness of cervical cancer, HPV and HPV vaccine, and willingness and acceptability to vaccinate in sub-Saharan African (SSA) countries. We further identified countries that fulfill the two GAVI Alliance eligibility criteria to support nationwide HPV vaccination.MethodsWe conducted a systematic review of peer-reviewed studies on the knowledge and awareness of cervical cancer, HPV and HPV vaccine, and willingness and acceptability to vaccinate. Trends in Diphtheria-tetanus-pertussis (DTP3) vaccine coverage in SSA countries from 1990–2011 were extracted from the World Health Organization database.FindingsThe review revealed high levels of willingness and acceptability of HPV vaccine but low levels of knowledge and awareness of cervical cancer, HPV or HPV vaccine. We identified only six countries to have met the two GAVI Alliance requirements for supporting introduction of HPV vaccine: 1) the ability to deliver multi-dose vaccines for no less than 50% of the target vaccination cohort in an average size district, and 2) achieving over 70% coverage of DTP3 vaccine nationally. From 2008 through 2011 all SSA countries, with the exception of Mauritania and Nigeria, have reached or maintained DTP3 coverage at 70% or above.ConclusionThere is an urgent need for more education to inform the public about HPV, HPV vaccine, and cervical cancer, particularly to key demographics, (adolescents, parents and healthcare professionals), to leverage high levels of willingness and acceptability of HPV vaccine towards successful implementation of HPV vaccination programs. There is unpreparedness in most SSA countries to roll out national HPV vaccination as per the GAVI Alliance eligibility criteria for supporting introduction of the vaccine. In countries that have met 70% DTP3 coverage, pilot programs need to be rolled out to identify the best practice and strategies for delivering HPV vaccines to adolescents and also to qualify for GAVI Alliance support.

The data is in CSV format and includes all historical data on the pandemic up to 03/01/2023, following a 1-line format per country and date.

In the pre-processing of these data, missing data were checked. It was observed, for example, that the missing data referring to new_cases was where the total number of cases had not been changed and that most of the missing data related to vaccination, which actually at the beginning of the pandemic there was no data. Therefore, to solve these cases of missing data it was decided to replace the data containing “NaN” by zero. Some of these features were combined to generate new features. This process that creates new features (data) from existing data, aiming to improve the data before applying machine learning algorithms, is called feature engineering. The new features created were: - Vaccination rate (vaccination_ratio'): total number of people who received at least one dose of vaccine divided by the population at risk. This dose number was chosen because it has a higher correlation with new deaths. - Prevalence: existing cases of the disease at a given time divided by the population at risk of having the disease. Formula: COVID-19 cases ÷ Population at risk * 100. Example: 168,331 ÷ 210,000,000 * 100 = 0.08. - Incidence: new cases of the disease in a defined population during a specific period (one day, for example) divided by the population at risk. Formula: New COVID-19 cases in one day ÷ Population - Total cases * 100. Example: 5,632 ÷ 209,837,301 * 100 = 0.0026.

Statistics from SPC's Public Health Division (PHD) on COVID-19 vaccination in Pacific Island Countries and Territories. Monitoring the impact of COVID-19 and the effectiveness of prevention and control strategies remains a public health priority. With the COVID-19 Public Health Emergency of International Concern declaration ending, some metrics have changed in frequency, source, or availability (i.e vaccination data). SPC will no longer continue to provide updated information on vaccination. The last update for this dataset was the 09 May 2023.

Find more Pacific data on PDH.stat.

Regarding all Vaccination Data The date of Last Update is 4/21/2023. Additionally on 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. See this link for more information https://imap.maryland.gov/pages/covid-data Summary The cumulative number of COVID-19 vaccinations percent age group population: 16-17; 18-49; 50-64; 65 Plus. Description COVID-19 - Vaccination Percent Age Group Population data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet. COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county. Terms of Use The Spatial Data, and the information therein, (collectively the Data) is provided as is without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata. This map is for planning purposes only. MEMA does not guarantee the accuracy of any forecast or predictive elements.

Context

I collect recent tweets about the COVID-19 vaccines used in entire world on large scale, as following: * Pfizer/BioNTech;
* Sinopharm;
* Sinovac;
* Moderna;
* Oxford/AstraZeneca;
* Covaxin;
* Sputnik V.

Data collection

The data is collected using tweepy Python package to access Twitter API. For each of the vaccine I use relevant search term (most frequently used in Twitter to refer to the respective vaccine)

Data collection frequency

Initial data was merged from tweets about Pfizer/BioNTech vaccine. I added then tweets from Sinopharm, Sinovac (both Chinese-produced vaccines), Moderna, Oxford/Astra-Zeneca, Covaxin and Sputnik V vaccines. The collection was in the first days twice a day, until I identified approximatively the new tweets quota and then collection (for all vaccines) stabilized at once a day, during morning hours (GMT).

Inspiration

You can perform multiple operations on the vaccines tweets. Here are few possible suggestions:

• Study the subjects of recent tweets about the vaccine made by various producers;
• Perform various NLP tasks on this data source (topic modelling, sentiment analysis);
• Using the COVID-19 World Vaccination Progress (where we can see the progress of the vaccinations and the countries where the vaccines are administered), you can study the relationship between the vaccination progress and the discussions in social media (from the tweets) about the vaccines.

Context

Coronavirus infection is currently the most important health topic. It surely tested and continues to test to the fullest extent the healthcare systems around the world. Although big progress is made in handling this pandemic, a tremendous number of questions are needed to be answered. I hereby present to you the local Bulgarian COVID-19 dataset with some context. It could be used as a comparator because it stands out compared to other countries and deserves analysis.

Context for Bulgarian population: Population - 6 948 445 Median age - 44.7 years Aged >65 - 20.801 % Aged >70 - 13.272%

Summary of the results: - first pandemic wave was weak, probably because of the early state of emergency (5 days after the first confirmed case). Whether this was a good decision or it was too early and just postpone the inevitable is debatable. -healthcare system collapses (probably due to delayed measures) in the second and third waves which resulted in Bulgaria gaining the top ranks for mortality and morbidity tables worldwide and in the EU. - low percentage of vaccinated people results in a prolonged epidemic and delaying the lifting of the preventive measures.

Some of the important moments that should be considered when interpreting the data: 08.03.2020 - Bulgaria confirmed its first two cases. The government issued a nationwide ban on closed-door public events (first lockdown); 13.03.2020- after 16 reported cases in one day, Bulgaria declared a state of emergency for one month until 13.04.2020. Schools, shopping centres, cinemas, restaurants, and other places of business were closed. All sports events were suspended. Only supermarkets, food markets, pharmacies, banks, and gas stations remain open. 03.04.2020 - The National Assembly approved the government's proposal to extend the state of emergency by one month until 13.05.2020; 14.05.2020 - the national emergency was lifted, and in its place was declared a state of an emergency epidemic situation. Schools and daycares remain closed, as well as shopping centers and indoor restaurants; 18.05.2020 - Shopping malls and fitness centers opened; 01.06.2020 - Restaurants and gaming halls opened; 10.07.2020 - discos and bars are closed, the sports events are without an audience; 29.10.2020 - High school and college students are transitioning to online learning; 27.11.2020 - the whole education is online, restaurants, nightclubs, bars, and discos are closed (second lockdown 27.11 - 21.12); 05.12.2020 - the 14-day mortality rate is the highest in the world; 16.01.2021 - some of the students went back to school; 01.03.2021 - restaurants and casinos opened; 22.03.2021 - restaurants, shopping malls, fitness centers, and schools are closed (third lockdown for 10 days - 22.03 - 31.03); 19.04.2021 - children daycare facilities, fitness centers, and nightclubs are opened;

Content

This dataset consists of 447 rows with 29 columns and covers the period 08.03.2020 - 28.05.2021. In the beginning, there are some missing values until the proper statistical report was established.

Inspiration

A publication proposal is sent to anyone who wishes to collaborate. Based on the results and the value of the findings and the relevance of the topic it is expected to publish: - in a local journal (guaranteed); - in a SCOPUS journal (highly probable); - in an IF journal (if the results are really insightful).

The topics could be, but not limited to: - descriptive analysis of the pandemic outbreak in the country; - prediction of the pandemic or the vaccination rate; - discussion about the numbers compared to other countries/world; - discussion about the government decisions; - estimating cut-off values for step-down or step-up of the restrictions.

Error or query reporting

If you find an error, have a question, or wish to make a suggestion, I encourage you to reach me.

Vaccination against COVID-19 is making progress globally, but vaccine doses remain a rare commodity in many parts of the world. New virus variants mean that updated vaccines become available more slowly. Policymakers have defined criteria to regulate who gets priority access to the vaccination, such as age, health complications, or those who hold system-relevant jobs. But how does the public think about vaccine allocation? To explore those preferences, we surveyed respondents in Brazil, Germany, Italy, Poland, and the United States from September to December of 2020 using ranking and forced-choice tasks. We find that public preferences are consistent with expert guidelines prioritizing health care workers and people with medical preconditions. However, the public also considers those signing up early for vaccination and citizens of the country to be more deserving than later-comers and non-citizens. These results hold across measures, countries, and socio-demographic subgroups.

Series Name: (S.4.C.13) Extent to which countries have laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care information and education: Component 13: HPV Vaccine (percent)Series Code: SH_LGR_ACSRHEC13Release Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 5.6.2: Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and educationTarget 5.6: Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferencesGoal 5: Achieve gender equality and empower all women and girlsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

This publication corresponds to the Common Data Model (CDM) specification of the Baseline Use Case proposed in T.5.2 (WP5) in the BY-COVID project on “SARS-CoV-2 Vaccine(s) effectiveness in preventing SARS-CoV-2 infection.” Research Question: “How effective have the SARS-CoV-2 vaccination programmes been in preventing SARS-CoV-2 infections?” Intervention (exposure): COVID-19 vaccine(s) Outcome: SARS-CoV-2 infection Subgroup analysis: Vaccination schedule (type of vaccine) Study Design: An observational retrospective longitudinal study to assess the effectiveness of the SARS-CoV-2 vaccine in preventing SARS-CoV-2 infections using routinely collected social, health and care data from several countries. A causal model was established using Directed Acyclic Graphs (DAGs) to map domain knowledge, theories and assumptions about the causal relationship between exposure and outcome. The DAG developed for the research question of interest is shown below. Cohort definition: All people eligible to be vaccinated (from 5 to 115 years old, included) or with, at least, one dose of a SARS-CoV-2 vaccine (any of the available brands) having or not a previous SARS-CoV-2 infection. Inclusion criteria: All people vaccinated with at least one dose of the COVID-19 vaccine (any available brands) in an area of residence. Any person eligible to be vaccinated (from 5 to 115 years old, included) with a positive diagnosis (irrespective of the type of test) for SARS-CoV-2 infection (COVID-19) during the period of study. Exclusion criteria: People not eligible for the vaccine (from 0 to 4 years old, included) Study period: From the date of the first documented SARS-CoV-2 infection in each country to the most recent date in which data is available at the time of analysis. Roughly from 01-03-2020 to 30-06-2022, depending on the country. Files included in this publication: Causal model (responding to the research question) SARS-CoV-2 vaccine effectiveness causal model v.1.0.0 (HTML) - Interactive report showcasing the structural causal model (DAG) to answer the research question SARS-CoV-2 vaccine effectiveness causal model v.1.0.0 (QMD) - Quarto RMarkdown script to produce the structural causal model Common data model specification (following the causal model) SARS-CoV-2 vaccine effectiveness data model specification (XLXS) - Human-readable version (Excel) SARS-CoV-2 vaccine effectiveness data model specification dataspice (HTML) - Human-readable version (interactive report) SARS-CoV-2 vaccine effectiveness data model specification dataspice (JSON) - Machine-readable version Synthetic dataset (complying with the common data model specifications) SARS-CoV-2 vaccine effectiveness synthetic dataset (CSV) [UTF-8, pipe | separated, N~650,000 registries] SARS-CoV-2 vaccine effectiveness synthetic dataset EDA (HTML) - Interactive report of the exploratory data analysis (EDA) of the synthetic dataset SARS-CoV-2 vaccine effectiveness synthetic dataset EDA (JSON) - Machine-readable version of the exploratory data analysis (EDA) of the synthetic dataset SARS-CoV-2 vaccine effectiveness synthetic dataset generation script (IPYNB) - Jupyter notebook with Python scripting and commenting to generate the synthetic dataset #### Baseline Use Case: SARS-CoV-2 vaccine effectiveness assessment - Common Data Model Specification v.1.1.0 change log #### Updated Causal model to eliminate the consideration of 'vaccination_schedule_cd' as a mediator Adjusted the study period to be consistent with the Study Protocol Updated 'sex_cd' as a required variable Added 'chronic_liver_disease_bl' as a comorbidity at the individual level Updated 'socecon_lvl_cd' at the area level as a recommended variable Added crosswalks for the definition of 'chronic_liver_disease_bl' in a separate sheet Updated the 'vaccination_schedule_cd' reference to the 'Vaccine' node in the updated DAG Updated the description of the 'confirmed_case_dt' and 'previous_infection_dt' variables to clarify the definition and the need for a single registry per person The scripts (software) accompanying the data model specification are offered "as-is" without warranty and disclaiming liability for damages resulting from using it. The software is released under the CC-BY-4.0 licence, which permits you to use the content for almost any purpose (but does not grant you any trademark permissions), so long as you note the license and give credit.

Attitudes on vaccination against COVID-19. Topics: preferred time for getting vaccinated; importance of each of the following issues with regard to getting vaccinated: vaccine will help to end the pandemic, vaccine will protect respondent from getting COVID-19, vaccine will protect relatives and others from getting COVID-19, vaccine will make it possible to resume a more normal professional life, vaccine will make it possible to travel, vaccine will make it possible to meet family and friends, vaccine will make it possible to go to restaurants, cinemas etc.; importance of each of the following issues with regard to not getting vaccinated: pandemic will be over soon, personal risk of being infected is very low, risk posed by COVID-19 in general is exaggerated, worries about side effects of COVID-19 vaccines, vaccines have not been sufficiently tested yet, vaccines are ineffective, against vaccines in general; factors to increase personal willingness of getting vaccinated: more people around doing it, more people have already been vaccinated and we see that there are no major side-effects, people that recommend the vaccines are vaccinated themselves, doctor recommends respondent to do so, vaccines are developed in the European Union, full clarity on how vaccines are being developed, tested and authorized, respondent is very eager to get vaccinated or is already vaccinated, won’t get vaccinated anyway; attitude towards the following statements on the vaccines: benefits outweigh possible risks, vaccines authorised in the European Union are safe, vaccines are being developed, tested and authorised too quickly to be safe, vaccines could have long term side-effects that we do not know yet, a vaccine is the only way to end the pandemic, no understanding why people are reluctant to get vaccinated, serious diseases have disappeared thanks to vaccines; attitude towards the following statements: one can avoid being infected without being vaccinated, public authorities are not sufficiently transparent about COVID-19 vaccines, getting vaccinated against COVID-19 is a civic duty, vaccination should be compulsory, European Union is playing a key role in ensuring access to COVID-19 vaccines in the own country; most trustworthy institutions or persons regarding the provision of information about COVID-19 vaccines; interest in additional information about the following aspects: development, testing, and authorization of COVID-19 vaccines, safety of COVID-19 vaccines, effectiveness of COVID-19 vaccines; satisfaction with the handling of the vaccination strategy by: national government, EU; applicability of the following statements: respondent knows people who have tested positive to COVID-19, respondent knows people who have been ill because of COVID-19, respondent has tested positive to COVID-19, respondent has been ill because of COVID-19, respondent fears to be infected in the future; vaccination of respondent: as a child, as an adult; attitude towards vaccines in general: are safe, are effective. Demography: age; sex; nationality; age at end of education; occupation; professional position; type of community; household composition and household size; region. Additionally coded was: respondent ID; country; device used for interview; nation group; weighting factor. Einstellungen zur Impfung gegen Covid-19. Themen: präferierter Impfzeitpunkt; Wichtigkeit der folgenden Gründe im Hinblick auf die Entscheidung, sich impfen zu lassen: Impfstoff wird bei der Beendigung der Pandemie helfen, Impfstoff wird den/die Befragte/n vor Covid-19 schützen, Impfstoff wird Verwandte und andere vor COVID-19 schützen, Impfstoff wird wieder ein normaleres Berufsleben ermöglichen, Impfstoff wird das Reisen ermöglichen, Impfstoff wird Treffen mit Familie und Freunden ermöglichen, Impfstoff wird Restaurantbesuche und andere Aktivitäten wieder ermöglichen; Wichtigkeit der folgenden Gründe im Hinblick auf die Entscheidung, sich nicht impfen zu lassen: Pandemie wird bald vorbei sein, persönliches Infektionsrisiko ist sehr gering, Risiko durch COVID-19 ist allgemein übertrieben, Sorgen über die Nebenwirkungen von COVID-19-Impfstoffen, Impfstoffe sind noch nicht ausreichend getestet, Impfstoffe sind unwirksam, generelle Ablehnung von Impfungen; Faktoren, die die persönliche Impfbereitschaft erhöhen würden: mehr geimpfte Menschen im Umfeld, viele erfolgreich geimpfte Menschen ohne gravierende Nebenwirkungen, Menschen, die die Impfung empfehlen, sind selbst geimpft, Empfehlung des eigenen Arztes, Entwicklung der Impfstoffe in der Europäischen Union, vollständige Klarheit über Entwicklung, Testung und Zulassung der Impfstoffe, starker Wunsch nach einer Impfung bzw. Befragte/r ist bereits geimpft, keine Impfung geplant; Einstellung zu den folgenden Aussagen zu den Impfstoffen: Vorteile überwiegen mögliche Risiken, in der EU zugelassene Impfstoffe sind sicher, zu schnelle Entwicklung, Testung und Zulassung der Impfstoffe, um sicher zu sein, noch unbekannte potentielle Langzeit-Nebenwirkungen, Impfung ist die einzige Möglichkeit zur Beendigung der Pandemie, kein Verständnis für Impfgegner, Ausrottung ernsthafter Krankheiten durch Impfung; Einstellung zu den folgenden Aussagen: Ansteckung kann auch ohne Impfung vermieden werden, mangelnde Transparenz öffentlicher Behörden in Bezug auf die Corona-Impfstoffe, Impfung gegen COVID-19 ist Bürgerpflicht, Impfung sollte verpflichtend sein, Europäische Union spielt wesentliche Rolle bei der Versorgung des eigenen Landes mit Impfstoff; vertrauenswürdigste Institutionen oder Personen im Hinblick auf die Bereitstellung von Informationen über Corona-Impfstoffe; Interesse an zusätzlichen Informationen über die folgenden Aspekte: Entwicklung, Testung und Zulassung von COVID-19-Impfstoffen, Sicherheit von COVID-19- Impfstoffen, Effektivität von COVID-19-Impfstoffen; Zufriedenheit mit der Umsetzung der Impfstrategie durch: nationale Regierung, EU; Anwendbarkeit der folgenden Aussagen: Befragte/r kennt Menschen mit positivem Corona-Testergebnis, Befragte/r kennt Menschen mit Corona-Erkrankung, Befragte/r hatte positives Corona-Testergebnis, Befragte/r war an Corona erkrankt, Befragte/r fürchtet Ansteckung in der Zukunft; Impfung des/der Befragten als: Kind, Erwachsener; Einstellung zu Impfstoffen im allgemeinen: sind sicher, sind wirksam. Demographie: Alter; Geschlecht; Staatsangehörigkeit; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Urbanisierungsgrad; Haushaltszusammensetzung und Haushaltsgröße; Region. Zusätzlich verkodet wurde: Befragten-ID; Land; für das Interview genutztes Gerät; Nationengruppe; Gewichtungsfaktor.

Content

This dataset is a result of survey data generated from respondents to an Ipsos survey asking the question:"If a vaccine for COVID-19 were available, I would get it," on its Global Advisor online platform between 2020-07-24 to 2020-08-07 compared to data gathered between 2020-10-08 to 2020-10-13. August 2020 data is gathered from approximately 13,500 respondents and the October 2020 data is gathered from 18,526 respondents, both from adults aged 16-74 from 15 countries.

"The data is weighted so that each country’s sample composition best reflects the demographic profile of the adult population according to the most recent census data."

"Where results do not sum to 100 or the ‘difference’ appears to be +/-1 more/less than the actual, this may be due to rounding, multiple responses or the exclusion of don't knows or not stated responses."

"The precision of Ipsos online polls is calculated using a credibility interval with a poll of 1,000 accurate to +/- 3.5 percentage points and of 500 accurate to +/- 4.8 percentage points. For more information on the Ipsos use of credibility intervals, please visit the Ipsos website."

"The publication of these findings abides by local rules and regulations."

Methodology GLOBAL ATTITUDES ON A COVID-19 VACCINE

Article COVID-19 vaccination intent is decreasing globally

Einstellungen zur Impfung gegen Covid-19. Themen: Befragte/r wurde gegen das Coronavirus geimpft; präferierter Zeitpunkt für die Booster-Impfung; präferierter Impfzeitpunkt; Wichtigkeit der folgenden Gründe im Hinblick auf die Entscheidung, sich impfen zu lassen: Impfstoff wird bei der Beendigung der Pandemie helfen, Impfstoff wird den/die Befragte/n vor schweren Verläufen schützen, Impfstoff wird Verwandte und andere vor schweren Verläufen schützen, Impfstoff wird wieder ein normaleres Berufsleben ermöglichen, Impfstoff wird das Reisen ermöglichen, Impfstoff wird Treffen mit Familie und Freunden ermöglichen, Impfstoff wird Restaurantbesuche und andere Aktivitäten wieder ermöglichen; Wichtigkeit der folgenden Gründe im Hinblick auf die Entscheidung, sich nicht impfen zu lassen: Pandemie wird bald vorbei sein, persönliches Infektionsrisiko ist sehr gering, Risiko durch COVID-19 ist allgemein übertrieben, Sorgen über die Nebenwirkungen von COVID-19-Impfstoffen, Impfstoffe sind noch nicht ausreichend getestet, Impfstoffe sind unwirksam, generelle Ablehnung von Impfungen; Faktoren, die die persönliche Impfbereitschaft erhöhen würden: mehr geimpfte Menschen im Umfeld, viele erfolgreich geimpfte Menschen ohne gravierende Nebenwirkungen, Menschen, die die Impfung empfehlen, sind selbst geimpft, Empfehlung des eigenen Arztes, Entwicklung der Impfstoffe in der Europäischen Union, vollständige Klarheit über Entwicklung, Testung und Zulassung der Impfstoffe, starker Wunsch nach einer Impfung bzw. Befragte/r ist bereits geimpft, keine Impfung geplant; Einstellung zu den folgenden Aussagen zu den Impfstoffen: Vorteile überwiegen mögliche Risiken, in der EU zugelassene Impfstoffe sind sicher, zu schnelle Entwicklung, Testung und Zulassung der Impfstoffe, um sicher zu sein, noch unbekannte potentielle Langzeit-Nebenwirkungen, Impfung ist die einzige Möglichkeit zur Beendigung der Pandemie, kein Verständnis für Impfgegner, Ausrottung ernsthafter Krankheiten durch Impfung; Einstellung zu den folgenden Aussagen: Ansteckung kann auch ohne Impfung vermieden werden, mangelnde Transparenz öffentlicher Behörden in Bezug auf die Corona-Impfstoffe, Impfung gegen COVID-19 ist Bürgerpflicht, Impfung sollte verpflichtend sein, Europäische Union spielt wesentliche Rolle bei der Versorgung des eigenen Landes mit Impfstoff; Einstellung zu den folgenden Aussagen: Schwierigkeit des Findens vertrauenswürdiger Informationen über COVID-19 und die Impfstoffe, Impfung von Kindern gegen COVID-19 ist gut, Zugangsbeschränkungen für Impfgegner bei besonderen Veranstaltungen oder an besonderen Plätzen sind akzeptabel, Zugang aller Staaten zu Impfstoffen ist für die Beendigung der Pandemie essentiell; vertrauenswürdigste Institutionen oder Personen im Hinblick auf die Bereitstellung von Informationen über Corona-Impfstoffe; Interesse an zusätzlichen Informationen über die folgenden Aspekte: Entwicklung, Testung und Zulassung von COVID-19-Impfstoffen, Sicherheit von COVID-19- Impfstoffen, Effektivität von COVID-19-Impfstoffen, Nutzung der Impfstoffe für bestimmte Personengruppen; Zufriedenheit mit der Umsetzung der Impfstrategie durch: nationale Regierung, EU; Anwendbarkeit der folgenden Aussagen: Befragte/r kennt Menschen mit positivem Corona-Testergebnis, Befragte/r kennt Menschen mit Corona-Erkrankung, Befragte/r hatte positives Corona-Testergebnis, Befragte/r war an Corona erkrankt, Befragte/r fürchtet Ansteckung in der Zukunft; Impfung des/der Befragten als: Kind, Erwachsener; Einstellung zu Impfstoffen im allgemeinen: sind sicher, sind wirksam. Demographie: Staatsangehörigkeit; Urbanisierungsgrad; Alter; Geschlecht; Alter bei Beendigung der Ausbildung; Beruf; berufliche Stellung; Haushaltszusammensetzung und Haushaltsgröße; Region. Zusätzlich verkodet wurde: Befragten-ID; Land; für das Interview genutztes Gerät; Nationengruppe; Gewichtungsfaktor. Attitudes on vaccination against Covid-19. Topics: respondent has been vaccinated against COVID-19; preferred time for getting a booster dose; preferred time for getting vaccinated; importance of each of the following issues with regard to getting vaccinated: vaccine will help to end the pandemic, vaccine will protect respondent from severe forms of disease, vaccine will protect relatives and others from severe forms of disease, vaccine will make it possible to resume a more normal professional life, vaccine will make it possible to travel, vaccine will make it possible to meet family and friends, vaccine will make it possible to go to restaurants, cinemas etc.; importance of each of the following issues with regard to not getting vaccinated: pandemic will be over soon, personal risk of being infected is very low, risk posed by Covid-19 in general is exaggerated, worries about side effects of Covid-19 vaccines, vaccines have not been sufficiently tested yet, vaccines are ineffective, against vaccines in general; factors to increase personal willingness of getting vaccinated: more people around doing it, more people have already been vaccinated and we see that there are no major side-effects, people that recommend the vaccines are vaccinated themselves, doctor recommends respondent to do so, vaccines are developed in the European Union, full clarity on how vaccines are being developed, tested and authorized, respondent is very eager to get vaccinated or is already vaccinated, won’t get vaccinated anyway; attitude towards the following statements on the vaccines: benefits outweigh possible risks, vaccines authorised in the European Union are safe, vaccines are being developed, tested and authorised too quickly to be safe, vaccines could have long term side-effects that we do not know yet, a vaccine is the only way to end the pandemic, no understanding why people are reluctant to get vaccinated, serious diseases have disappeared thanks to vaccines; attitude towards the following statements: one can avoid being infected without being vaccinated, public authorities are not sufficiently transparent about COVID-19 vaccines, getting vaccinated against COVID-19 is a civic duty, vaccination should be compulsory, European Union is playing a key role in ensuring access to COVID-19 vaccines in the own country; attitude towards the following statements: difficult to find trustworthy information about COVID-19 and vaccines, good to vaccinate children against COVID-19, acceptable to restrict access to special events or places for people who refuse to get vaccinated, crucial that all countries in the world can have access to vaccine to end the pandemic; most trustworthy institutions or persons regarding the provision of information about COVID-19 vaccines; interest in additional information about the following aspects: development, testing, and authorization of COVID-19 vaccines, safety of COVID-19 vaccines, effectiveness of COVID-19 vaccines, use of COVID-19 vaccines for specific groups of persons; satisfaction with the handling of the vaccination strategy by: national government, EU; applicability of the following statements: respondent knows people who have tested positive to COVID-19, respondent knows people who have been ill because of COVID-19, respondent has tested positive to COVID-19, respondent has been ill because of COVID-19, respondent fears to be infected in the future; vaccination of respondent: as a child, as an adult; attitude towards vaccines in general: are safe, are effective. Demography: nationality; type of community; age; sex; age at end of education; occupation; professional position; household composition and household size; region. Additionally coded was: respondent ID; country; device used for interview; nation group; weighting factor.

BackgroundImmunizations are one of the most effective tools a community can use to increase overall health and decrease the burden of vaccine-preventable diseases. Nevertheless, socioeconomic status, geographical location, education, and a child's sex have been identified as contributing to inequities in vaccine uptake in low- and middle-income countries (LMICs). Madagascar follows the World Health Organization's Extended Programme on Immunization (EPI) schedule, yet vaccine distribution remains highly inequitable throughout the country. This systematic review sought to understand the differences in EPI vaccine uptake between boys and girls in Madagascar.MethodsA systematic literature search was conducted in August 2021 through MEDLINE, the Cochrane Library, Global Index Medicus, and Google Scholar to identify articles reporting sex-disaggregated vaccination rates in Malagasy children. Gray literature was also searched for relevant data. All peer-reviewed articles reporting sex-disaggregated data on childhood immunizations in Madagascar were eligible for inclusion. Risk of bias was assessed using a tool designed for use in systematic reviews. Data extraction was conducted with a pre-defined data extraction tool. Sex-disaggregated data were synthesized to understand the impact of a child's sex on vaccination status.FindingsThe systematic search identified 585 articles of which a total of three studies were included in the final data synthesis. One additional publication was included from the gray literature search. Data from included articles were heterogeneous and, overall, indicated similar vaccination rates in boys and girls. Three of the four articles reported slightly higher vaccination rates in girls than in boys. A meta-analysis was not conducted due to the heterogeneity of included data. Six additional barriers to immunization were identified: socioeconomic status, mother's education, geographic location, supply chain issues, father's education, number of children in the household, and media access.InterpretationThe systematic review revealed the scarcity of available sex-stratified immunization data for Malagasy children. The evidence available was limited and heterogeneous, preventing researchers from conclusively confirming or denying differences in vaccine uptake based on sex. The low vaccination rates and additional barriers identified here indicate a need for increased focus on addressing the specific obstacles to vaccination in Madagascar. A more comprehensive assessment of sex-disaggregated vaccination status of Malagasy children and its relationship with such additional obstacles is recommended. Further investigation of potential differences in vaccination status will allow for the effective implementation of strategies to expand vaccine coverage in Madagascar equitably.Funding and registrationAH, BT, FM, GN, and RR are supported by a grant from the Bill and Melinda Gates Foundation (grant number: OPP1205877). The review protocol is registered in the Prospective Register of Systematic Reviews (PROSPERO ID: CRD42021265000).

Rotavirus is the most common pathogen causing pediatric diarrhea and an important cause of morbidity and mortality in low- and middle-income countries. Previous evidence suggests that the introduction of rotavirus vaccines in national immunization schedules resulted in dramatic declines in disease burden but may also be changing the rotavirus genetic landscape and driving the emergence of new genotypes. We report genotype data of more than 16,000 rotavirus isolates from 40 countries participating in the Global Rotavirus Surveillance Network. Data from a convenience sample of children under five years of age hospitalized with acute watery diarrhea who tested positive for rotavirus were included. Country results were weighted by their estimated rotavirus disease burden to estimate regional genotype distributions. Globally, the most frequent genotypes identified after weighting were G1P8, G1P6 and G3P8. Genotypes varied across WHO Regions and between countries that had and had not introduced rotavirus vaccine. G1P[8] was less frequent among African (36 vs 20%) and European (33 vs 8%) countries that had introduced rotavirus vaccines as compared to countries that had not introduced. Our results describe differences in the distribution of the most common rotavirus genotypes in children with diarrhea in low- and middle-income countries. G1P[8] was less frequent in countries that had introduced the rotavirus vaccine while different strains are emerging or re-emerging in different regions.

The 1998 Lancet paper by Wakefield et al., despite subsequent retraction and evidence indicating no causal link between vaccinations and autism, triggered significant parental concern. The aim of this study was to analyze the online information available on this topic. Using localized versions of Google, we searched “autism vaccine” in English, French, Italian, Portuguese, Mandarin, and Arabic and analyzed 200 websites for each search engine result page (SERP). A common feature was the newsworthiness of the topic, with news outlets representing 25–50% of the SERP, followed by unaffiliated websites (blogs, social media) that represented 27–41% and included most of the vaccine-negative websites. Between 12 and 24% of websites had a negative stance on vaccines, while most websites were pro-vaccine (43–70%). However, their ranking by Google varied. While in Google.com, the first vaccine-negative website was the 43rd in the SERP, there was one vaccine-negative webpage in the top 10 websites in both the British and Australian localized versions and in French and two in Italian, Portuguese, and Mandarin, suggesting that the information quality algorithm used by Google may work better in English. Many webpages mentioned celebrities in the context of the link between vaccines and autism, with Donald Trump most frequently. Few websites (1–5%) promoted complementary and alternative medicine (CAM) but 50–100% of these were also vaccine-negative suggesting that CAM users are more exposed to vaccine-negative information. This analysis highlights the need for monitoring the web for information impacting on vaccine uptake.